Label-based OOD detection methods discriminate samples that differ from those in the training distribution. We focus on unsupervised OOD detection in this work, i.e., we do not assume access to data labeled as OOD. Unsupervised OOD detection methods include density-based [1]}, [2]}, [3]}, [4]}, [5]}, [6]}, [7]}, [8]}, [9]}, [10]}, reconstruction-based [11]}, [12]}, [13]}, [14]}, [15]}, [4]}, one-class classifiers [17]}, [18]}, self-supervised [19]}, [20]}, [21]}, [22]}, and supervised approaches [23]}, [24]}, though some works do not fall into any of these categories [25]}.
